15 posts published in the last hour 05:35SPEM: Structure-Aware Procedural Memory for Efficient Skill Use in LLM Agents 05:35Does Multi-Agent Collaboration Reduce Hallucination in Clinical Tasks? 05:35M-Plan: Comprehensive Planning For LLM Agents with Memory Arrangement 05:34Physical AI: Concepts, Techniques, and…
SPEM: Structure-Aware Procedural Memory for Efficient Skill Use in LLM Agents
arXiv:2610.26995v1 Announce Type: new Abstract: LLM-based agents increasingly rely on reusable skills. However, scaling skill libraries makes efficient…
Does Multi-Agent Collaboration Reduce Hallucination in Clinical Tasks?
arXiv:2610.27027v1 Announce Type: new Abstract: Multi-agent LLM systems improve performance on clinical tasks, and that improvement is often read as…
M-Plan: Comprehensive Planning For LLM Agents with Memory Arrangement
arXiv:2610.27032v1 Announce Type: new Abstract: Large language model (LLM)-based agents have recently demonstrated outstanding performance in single-turn…
Physical AI: Concepts, Techniques, and Challenges
arXiv:2610.27045v1 Announce Type: new Abstract: In recent years, physical artificial intelligence (physical AI) has experienced rapid development,…
Google Cloud Ships llm-d 1.0 With Cache-Aware Routing for TPU and GPU Fleets
Google, Red Hat and IBM released llm-d 1.0, an open-source Kubernetes-native stack for distributed LLM inference. It adds multi-tenant flow control,…
Teaching AI Agent to Play Poker Strategically Through Preference Optimization
arXiv:2610.27028v1 Announce Type: new Abstract: A poker agent can make a legal move and still make a strategically poor one. We ask whether compact AI…
When Planning Problems Become Hard for LLMs: Separating Structural Complexity from Semantic Interpretability
arXiv:2610.26964v1 Announce Type: new Abstract: Large language models (LLMs) struggle with classical planning, but benchmarks rarely distinguish…
MAMath-Bench: Process-Aware and Robust Evaluation of Multimodal Multi-Agent Mathematical Reasoning
arXiv:2610.26943v1 Announce Type: new Abstract: Mathematical reasoning is a fundamental capability for educational applications of Large Language Models…
AI for the Fortune 2000: How Tenex Builds an AI-Native SDLC — Alex Lieberman & CJ Hess
Listen now (44 mins) | Paying for outcomes, building with agents, and the last mile of enterprise AI adoption
LightCRS: Lightweight Multi-Domain Conversational Recommendation System
arXiv:2610.26948v1 Announce Type: new Abstract: Conversational recommender systems have recently adopted vision-language models to match user dialogue…
A Coding Guide to Cactus Whistle: Does a 16.9 MB Speech Model Hold Up Against Whisper Base? Word Error Rate, Speed, Streaming, Timestamps, Keywords, and Speech-to-Tool Calls on a CPU
Explore a comprehensive coding guide benchmarking Cactus Compute’s 16.9 MB speech model against Whisper base. Learn about word error rates, CPU speed,…
Fix, Not Just Resample: Bounded Compiler-Guided Repair in Lean 4
arXiv:2610.26942v1 Announce Type: new Abstract: Automated theorem provers often rely on sampling language models, but proof assistants like Lean reject…
NVIDIA Nsight Python Tutorial: How to Profile GPU Kernels
Discover NVIDIA Nsight Python, an open-source interface that allows developers to profile GPU kernels, analyze hardware metrics, evaluate cache and clock…
Industrial Agentic AI: LLM-Driven New-Generation Agents for Smart Manufacturing
arXiv:2610.26941v1 Announce Type: new Abstract: Large language models (LLMs) are enabling agents to move beyond predefined workflows toward autonomous…
AI News Brief Hourly Summary 2026-10-22 07h : 33 posts
33 posts published in the last hour 04:35eMudhra Intermediate CA Compromise 04:35Terminal X matches senior investor judgment 88.6% with GPT-6.1 04:35Navalia: Advancing Cowork Agents through Verifiable Task Synthesis and Long-Horizon Post-Training 04:35Southeast Asia’s digital economy to hit $368 billion 04:35Meta’s…
eMudhra Intermediate CA Compromise
eMudhra Intermediate CA Compromise This is very, very bad news: The private keys of eight eMudhra intermediate CA certificates have been compromised. They…
Terminal X matches senior investor judgment 88.6% with GPT-6.1
Terminal X’s Eval Agent grades outputs against a 54-criterion rubric, catching 25% more deviances from a fund’s mandate with GPT-6.1 Sol.